Cybersecurity projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Cybersecurity projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Cybersecurity Projects
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A Novel Feature Encoding Scheme for Machine Learning Based Malware Detection Systems
This project aims to improve malware detection by focusing on how data features are encoded before training machine learning models. It introduces a new entropy-based feature encoding method that enhances the accuracy and stability of malware classification. The approach is tested on benchmark datasets such as KDDCUP99, UNSW-NB15, and CIC-Evasive-PDFMal2022 to evaluate performance. Results show that models using the proposed encoding achieve higher F1 scores compared to traditional encoding techniques. The study also examines how different encodings affect the importance of features in malware detection. -
A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0
The project aims to improve cybersecurity in wireless sensor networks used in Industry 4.0. It focuses on detecting and preventing cyber-attacks in real time. Machine learning and deep learning algorithms are applied to classify and prioritize threats. The framework uses Decision Tree and MLP models for multi-class intrusion detection and an Autoencoder for binary classification. The goal is to provide accurate, intelligent, and prioritized protection for industrial networks. -
Event-Based Moving Target Defense in Cloud Computing with VM Migration: A Performance Modeling Approach
This project focuses on improving computer security in cloud systems. It uses a method that changes system settings, like moving virtual machines or changing IP addresses, to confuse attackers. The study models these changes to find the best ways to keep systems safe. It shows that using event-based detection works well when the system can accurately detect threats more than half the time. -
Deep Learning based Efficient Edge Slicing for System Cost Minimization in Wireless Networks
This project focuses on making future wireless networks smarter and more efficient. It uses advanced computing at the network edge to manage resources and choose which users to serve. The system predicts network traffic and allocates resources in advance. Overall, it reduces costs while meeting the needs of different applications. -
A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems
This project focuses on protecting cyber-physical systems, which are systems where computers control real-world devices. These systems are vulnerable to cyber-attacks, which are hard to detect. The project studies how deep learning can be used to identify attacks effectively. It also reviews existing methods and discusses future challenges in this area. -
Survey: Intrusion Detection System in Software-Defined Networking
This project studies the security challenges in modern computer networks called Software-Defined Networks (SDN). It looks at how these networks, while flexible and efficient, are vulnerable to attacks like DDoS and SQL injection. The work analyses these threats and suggests ways to build better intrusion detection systems. The goal is to make SDN networks safer and more reliable for future use. -
SIMC 2.0: Improved Secure ML Inference Against Malicious Clients
This project improves the security and speed of machine learning predictions. The goal is to make sure a client only sees the result while the server learns nothing. The researchers developed new methods to make calculations faster and reduce data transfer. Their approach, SIMC 2.0, is much quicker and more efficient than earlier methods. -
A BERT-Enhanced Exploration of Web and Mobile Request Safety Through Advanced NLP Models and Hybrid Architectures
This project focuses on improving the security of web and mobile applications. It studies how machine learning models can detect whether online requests are safe or risky. The research compares different models and combines them to create a stronger system against cyber threats. The goal is to make digital platforms safer and more reliable for everyday users. -
A Comprehensive Survey Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
This project explains how artificial intelligence methods like machine learning, deep learning, and reinforcement learning help in protecting systems from cyberattacks. These methods can find hidden threats and improve security by learning from data. It also studies how tools like ChatGPT can be used both to enhance and to attack cybersecurity systems. The research highlights their benefits, challenges, and future importance in keeping data safe. -
A Filtering Model for Evidence Gathering in an SDN-Oriented Digital Forensic and Incident Response Context
This project focuses on improving the security of software-defined networks. It introduces a system that automatically detects unusual network activities and starts a digital investigation process. The system uses artificial intelligence to identify possible attacks and find their causes. It helps both technical and organizational teams respond faster and more effectively to cyber incidents. -
A Novel Approach Based on Machine Learning, Blockchain, and Decision Process for Securing Smart Grid
This project focuses on improving the security of smart grid networks using machine learning and blockchain. It ensures that all electricity data and device activities are stored safely without tampering. The system can detect both known and unknown attacks. It also uses an improved method to save energy while keeping the network secure. -
A Review of Recent Advances Challenges and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods
This project focuses on detecting threats that come from within an organization, such as employees misusing their access. It reviews traditional and modern methods used to identify such insider threats. The study shows that deep learning and language-based models are more effective in recognizing unusual or harmful behavior. It also suggests using time-based data to improve future threat detection.
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